51702e9f0f
MemPalace's default embedder (all-MiniLM-L6-v2) is English-only-trained. Cross-lingual cosine similarity on parallel-translated text averages 0.35 across DE/FR/HI/IT/KO/RU — vs 0.88 for embeddinggemma-300m ONNX (q8) with the semantic-similarity prefix. RU is the worst at 0.17, meaning a Russian memory and its identical English translation embed to nearly orthogonal vectors. Multilingual users effectively cannot retrieve their own memories. This commit adds embeddinggemma-300m as an opt-in alternative: * New EmbeddinggemmaONNX class implementing ChromaDB's EF protocol. Lazy-downloads model_quantized.onnx (~300 MB) via huggingface_hub on first use; cached under ~/.cache/huggingface/. Applies the sim prefix, runs onnxruntime inference, truncates to 384 dims via Matryoshka (MRL), L2-normalizes. * MRL truncation to 384d is intentional: matches MiniLM's vector width so collection schemas don't change, and validation showed 384d MRL actually outperforms full 768d on these similarity tasks (0.893 vs 0.881 avg) — known property of MRL training. * MEMPALACE_EMBEDDING_MODEL env (default "minilm" for back-compat). Switching models on an existing palace requires re-embedding — ChromaDB rejects reads with a mismatched EF name. Run `mempalace repair rebuild-index` after changing the value. * New optional dep group: pip install mempalace[multilingual] Adds huggingface_hub + tokenizers + numpy. Core deps unchanged. ONNX q8 validated lossless vs the Ollama gguf benchmarked previously (max delta 0.002 cos across 240 parallel pairs). |
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| .agents/plugins | ||
| .claude-plugin | ||
| .codex-plugin | ||
| .devcontainer | ||
| .github | ||
| assets | ||
| benchmarks | ||
| docs | ||
| examples | ||
| hooks | ||
| integrations/openclaw | ||
| landing | ||
| mempalace | ||
| tests | ||
| tools | ||
| website | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .python-version | ||
| AGENTS.md | ||
| CHANGELOG.md | ||
| CLAUDE.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| MISSION.md | ||
| README.md | ||
| ROADMAP.md | ||
| SECURITY.md | ||
| openarena-claim.txt | ||
| pyproject.toml | ||
| uv.lock | ||
README.md
[!CAUTION] Scam alert. The only official sources for MemPalace are this GitHub repository, the PyPI package, and the docs site at mempalaceofficial.com. Any other domain — including
mempalace.tech— is an impostor and may distribute malware. Details and timeline: docs/HISTORY.md.
[!IMPORTANT] 🚨 Claude Code sessions expire in 30 days w/out auto-save hooks wired! Read this →
MemPalace
Local-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
What it is
MemPalace stores your conversation history as verbatim text and retrieves it with semantic search. It does not summarize, extract, or paraphrase. The index is structured — people and projects become wings, topics become rooms, and original content lives in drawers — so searches can be scoped rather than run against a flat corpus.
The retrieval layer is pluggable. The current default is ChromaDB; the
interface is defined in mempalace/backends/base.py
and alternative backends can be dropped in without touching the rest of
the system.
Nothing leaves your machine unless you opt in.
Architecture, concepts, and mining flows: mempalaceofficial.com/concepts/the-palace.
Install
We recommend uv — uv tool install puts
the mempalace CLI in an isolated environment on your PATH:
uv tool install mempalace
mempalace init ~/projects/myapp
If you prefer pip, pip install mempalace still works.
Quickstart
# Mine content into the palace
mempalace mine ~/projects/myapp # project files
mempalace mine ~/.claude/projects/ --mode convos # Claude Code sessions (scope with --wing per project)
# Search
mempalace search "why did we switch to GraphQL"
# Load context for a new session
mempalace wake-up
For Claude Code, Gemini CLI, MCP-compatible tools, and local models, see mempalaceofficial.com/guide/getting-started.
Benchmarks
All numbers below are reproducible from this repository with the commands
in benchmarks/BENCHMARKS.md. Full
per-question result files are committed under benchmarks/results_*.
LongMemEval — retrieval recall (R@5, 500 questions):
| Mode | R@5 | LLM required |
|---|---|---|
| Raw (semantic search, no heuristics, no LLM) | 96.6% | None |
| Hybrid v4, held-out 450q (tuned on 50 dev, not seen during training) | 98.4% | None |
| Hybrid v4 + LLM rerank (full 500) | ≥99% | Any capable model |
The raw 96.6% requires no API key, no cloud, and no LLM at any stage. The hybrid pipeline adds keyword boosting, temporal-proximity boosting, and preference-pattern extraction; the held-out 98.4% is the honest generalisable figure.
The rerank pipeline promotes the best candidate out of the top-20
retrieved sessions using an LLM reader. It works with any reasonably
capable model — we have reproduced it with Claude Haiku, Claude Sonnet,
and minimax-m2.7 via Ollama Cloud (no Anthropic dependency). The gap
between raw and reranked is model-agnostic; we do not headline a "100%"
number because the last 0.6% was reached by inspecting specific wrong
answers, which benchmarks/BENCHMARKS.md flags as teaching to the test.
Other benchmarks (full results in benchmarks/BENCHMARKS.md):
| Benchmark | Metric | Score | Notes |
|---|---|---|---|
| LoCoMo (session, top-10, no rerank) | R@10 | 60.3% | 1,986 questions |
| LoCoMo (hybrid v5, top-10, no rerank) | R@10 | 88.9% | Same set |
| ConvoMem (all categories, 250 items) | Avg recall | 92.9% | 50 per category |
| MemBench (ACL 2025, 8,500 items) | R@5 | 80.3% | All categories |
We deliberately do not include a side-by-side comparison against Mem0, Mastra, Hindsight, Supermemory, or Zep. Those projects publish different metrics on different splits, and placing retrieval recall next to end-to-end QA accuracy is not an honest comparison. See each project's own research page for their published numbers.
Reproducing every result:
git clone https://github.com/MemPalace/mempalace.git
cd mempalace
uv sync --extra dev # or: pip install -e ".[dev]"
# see benchmarks/README.md for dataset download commands
uv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json
Knowledge graph
MemPalace includes a temporal entity-relationship graph with validity windows — add, query, invalidate, timeline — backed by local SQLite. Usage and tool reference: mempalaceofficial.com/concepts/knowledge-graph.
MCP server
29 MCP tools cover palace reads/writes, knowledge-graph operations, cross-wing navigation, drawer management, and agent diaries. Installation and the full tool list: mempalaceofficial.com/reference/mcp-tools.
Agents
Each specialist agent gets its own wing and diary in the palace.
Discoverable at runtime via mempalace_list_agents — no bloat in your
system prompt:
mempalaceofficial.com/concepts/agents.
Auto-save hooks
Two Claude Code hooks save periodically and before context compression: mempalaceofficial.com/guide/hooks.
For per-message recall on top of the file-level chunks the hooks produce,
run mempalace sweep <transcript-dir> periodically — it stores one
verbatim drawer per user/assistant message, idempotent and resume-safe.
Requirements
- Python 3.9+
- A vector-store backend (ChromaDB by default)
- ~300 MB disk for the default embedding model
No API key is required for the core benchmark path.
Docs
- Getting started → mempalaceofficial.com/guide/getting-started
- CLI reference → mempalaceofficial.com/reference/cli
- Python API → mempalaceofficial.com/reference/python-api
- Full benchmark methodology → benchmarks/BENCHMARKS.md
- Release notes → CHANGELOG.md
- Corrections and public notices → docs/HISTORY.md
Contributing
PRs welcome. See CONTRIBUTING.md.
License
MIT — see LICENSE.